The invention relates generally to positron emission tomography (PET) systems, and more particularly, to pre-computing a scatter sinogram look-up table during PET emission acquisition to correct for scatter.
PET scanners typically generate images depicting the distribution of positron-emitting nuclides in patients. The positron interacts with an electron in the body of the patient by annihilation, and then the electron-positron pair is converted into two photons. The photons are emitted in opposite directions along a line of response. The annihilation photons are detected by detectors that are placed on both sides of the line of response on a detector ring. The image is then generated based on the acquired emission data that includes the annihilation photon detection information.
A PET scanner typically includes a detector ring assembly including rings of detectors that encircle the patient. Coincidence detection circuits connect to the detectors and record only those photons that are detected within a narrow time window by detectors located on opposite sides of a line joining the detectors to the point of annihilation. These detections are deemed to have occurred “simultaneously” and termed coincidence events. The coincidence events indicate that a positron annihilations occurred along a line joining the two opposing detectors. The coincidence events detected by the PET detector ring assembly are typically stored within data structures called emission sinograms. An emission sinogram is a histogram of the detected coincidence events where each of a plurality of bins in the histogram represents a potential detector pair element. An image of the activity distribution within a patient's body is generated from the emission sinograms through a process called image reconstruction.
Some gamma rays are deflected from their original direction due to interaction with a body part before reaching the detectors. Such events are termed scatter events. It is desirable to reject the scatter events during the acquisition of emission sinograms, because the images generated using only the detected true coincidence events represent a true activity distribution of radio-activity in the scanned body part of the patient. Not rejecting the scatter events in the image reconstruction results in biased estimates of the activity distribution in the patient.
In order to correct for scattered coincidences in PET scanners, various scatter correction methods are known. One widely used scatter correction method, used for 2D PET acquisitions, involves making scatter estimates based on integral transforms of the emission data. However, the method does not always perform satisfactorily for 3D PET acquisitions where scatter fractions are much larger. For moderate levels of scatter, for example, as seen in brain scans (and smaller patients), scatter correction using function-fitting methods to the counts outside the object support has provided improved performance. However, for larger patients, the function-fitting methods are not robust enough as they over-estimate the scatter inside the object. Model based scatter correction methods generally perform well for patients of all sizes and 3D PET acquisitions. Known model-based scatter correction methods generally involve algorithms that use the measured PET emission and transmission sinograms to estimate the prevalence of scattered events. The output of these algorithms is the mean estimate of the scatter events, which are also stored into sinograms. The measured PET emission sinograms are corrected for scatter by subtracting the estimated scatter sinograms from the measured PET emission data. Therefore, the final reconstructed images are based mainly on true coincidence events and not on the scatter events.
However, known model based scatter correction algorithms use many simplifying assumptions that can reduce the accuracy of the scatter estimation model. Further, known model-based scatter correction methods are time consuming and involve intensive computations that are performed after the entire PET scan acquisition is completed because they use the measured emission data